Prevalence, mortality, cost, and disparities in transcatheter mitral valve repair and replacement in cancer patients: Artificial intelligence and propensity score national 5-year analysis of 7495 procedures.

INTRODUCTION: We conducted the first comprehensive evaluation of the therapeutic value and safety profile of transcatheter mitral edge-to-edge repair (TEER) and transcatheter mitral valve replacement (TMVR) in individuals concurrently afflicted with cancer. METHODS: Utilizing the National Inpatient Sample (NIS) dataset, we analyzed all adult hospitalizations between 2016 and 2020 (n = 148,755,036). The inclusion criteria for this retrospectively analyzed prospective cohort study were all adult hospitalizations (age 18 years and older). Regression and machine learning analyses in addition to model optimization were conducted using ML-PSr (Machine Learning-augmented Propensity Score adjusted multivariable regression) and BAyesian Machine learning-augmented Propensity Score (BAM-PS) multivariable regression. RESULTS: Of all adult hospitalizations, there were 5790 (0.004%) TMVRs and 1705 (0.001%) TEERs. Of the total TMVRs, 160 (2.76%) were done in active cancer. Of the total TEERs, 30 (1.76%) were done in active cancer. After the comparable rates of TEER/TMVR in active cancer in 2016, the prevalence of TEER/TMVR was significantly less in active cancer from 2017 to 2020 (2.61% versus 7.28% p < 0.001). From 2017 to 2020, active cancer significantly decreased the odds of receiving TEER or TMVR (OR 0.28, 95%CI 0.13-0.68, p = 0.008). In patients with active cancer who underwent TMVR/TEER, there were no significant differences in socio-economic disparities, mortality or total hospitalization costs. CONCLUSION: The presence of malignancy does not contribute to increased mortality, length of stay or procedural costs in TMVR or TEER. Whereas the prevalence of TMVR has increased in patients with active cancer, the utilization of TEER in the context of active cancer is declining despite a growing patient population.

Percutaneous Coronary Intervention Mortality, Cost, Complications, and Disparities after Radiation Therapy: Artificial Intelligence-Augmented, Cost Effectiveness, and Computational Ethical Analysis.

The optimal cardio-oncology management of radiation therapy and its complications are unknown despite the high patient and societal costs. This study is the first known nationally representative, multi-year, artificial intelligence and propensity score-augmented causal clinical inference and computational ethical and policy analysis of percutaneous coronary intervention (PCI) mortality, cost, and disparities including by primary malignancy following radiation therapy. Bayesian Machine learning-augmented Propensity Score translational (BAM-PS) statistics were conducted in the 2016-2020 National Inpatient Sample. Of the 148,755,036 adult hospitalizations, 2,229,285 (1.50%) had a history of radiation therapy, of whom, 67,450 (3.00%) had an inpatient AMI, and of whom, 18,400 (28.69%) underwent PCI. Post-AMI mortality, costs, and complications were comparable with and without radiation across cancers in general and across the 30 primary malignancies tested, except for breast cancer, in which PCI significantly increased mortality (OR 3.70, 95%CI 1.10-12.43, p = 0.035). In addition to significant sex, race, and insurance disparities, significant regional disparities were associated with nearly 50 extra inpatient deaths and over USD 500 million lost. This large clinical, cost, and pluralistic ethical analysis suggests PCI when clinically indicated should be provided to patients regardless of sex, race, insurance, or region to generate significant improvements in population health, cost savings, and social equity.

Artificial Intelligence-Augmented Propensity Score, Cost Effectiveness and Computational Ethical Analysis of Cardiac Arrest and Active Cancer with Novel Mortality Predictive Score.

Background and objectives: Little is known about outcome improvements and disparities in cardiac arrest and active cancer. We performed the first known AI and propensity score (PS)-augmented clinical, cost-effectiveness, and computational ethical analysis of cardio-oncology cardiac arrests including left heart catheterization (LHC)-related mortality reduction and related disparities. Materials and methods: A nationally representative cohort analysis was performed for mortality and cost by active cancer using the largest United States all-payer inpatient dataset, the National Inpatient Sample, from 2016 to 2018, using deep learning and machine learning augmented propensity score-adjusted (ML-PS) multivariable regression which informed cost-effectiveness and ethical analyses. The Cardiac Arrest Cardio-Oncology Score (CACOS) was then created for the above population and validated. The results informed the computational ethical analysis to determine ethical and related policy recommendations. Results: Of the 101,521,656 hospitalizations, 6,656,883 (6.56%) suffered cardiac arrest of whom 61,300 (0.92%) had active cancer. Patients with versus without active cancer were significantly less likely to receive an inpatient LHC (7.42% versus 20.79%, p < 0.001). In ML-PS regression in active cancer, post-arrest LHC significantly reduced mortality (OR 0.18, 95%CI 0.14−0.24, p < 0.001) which PS matching confirmed by up to 42.87% (95%CI 35.56−50.18, p < 0.001). The CACOS model included the predictors of no inpatient LHC, PEA initial rhythm, metastatic malignancy, and high-risk malignancy (leukemia, pancreas, liver, biliary, and lung). Cost-benefit analysis indicated 292 racial minorities and $2.16 billion could be saved annually by reducing racial disparities in LHC. Ethical analysis indicated the convergent consensus across diverse belief systems that such disparities should be eliminated to optimize just and equitable outcomes. Conclusions: This AI-guided empirical and ethical analysis provides a novel demonstration of LHC mortality reductions in cardio-oncology cardiac arrest and related disparities, along with an innovative predictive model that can be integrated within the digital ecosystem of modern healthcare systems to improve equitable clinical and public health outcomes.

Fractional Flow Reserve Cardio-Oncology Effects on Inpatient Mortality, Length of Stay, and Cost Based on Malignancy Type: Machine Learning Supported Nationally Representative Case-Control Study of 30 Million Hospitalizations.

Background and Objectives: There are no nationally representative studies of mortality and cost effectiveness for fractional flow reserve (FFR) guided percutaneous coronary interventions (PCI) in patients with cancer. Our study aims to show how this patient population may benefit from FFR-guided PCI. Materials and Methods: Propensity score matched analysis and backward propagation neural network machine learning supported multivariable regression was performed for inpatient mortality in this case-control study of the 2016 National Inpatient Sample (NIS). Regression results were adjusted for age, race, income, geographic region, metastases, mortality risk, and the likelihood of undergoing FFR versus non-FFR PCI. All analyses were adjusted for the complex survey design to produce nationally representative estimates. Results: Of the 30,195,722 hospitalized patients meeting criteria, 3.37% of the PCIs performed included FFR. In propensity score adjusted multivariable regression, FFR versus non-FFR PCI significantly reduced inpatient mortality (OR 0.47, 95%CI 0.35−0.63; p < 0.001) and length of stay (LOS) (in days; beta −0.23, 95%CI −0.37−−0.09; p = 0.001) while increasing cost (in USD; beta $5708.63, 95%CI, 3042.70−8374.57; p < 0.001), without significantly increasing complications overall. FFR versus non-FFR PCI did not specifically change cancer patients' inpatient mortality, LOS, or cost. However, FFR versus non-FFR PCI significantly increased inpatient mortality for Hodgkin's lymphoma (OR 52.48, 95%CI 7.16−384.53; p < 0.001) and rectal cancer (OR 24.38, 95%CI 2.24−265.73; p = 0.009). Conclusions: FFR-guided PCI may be safely utilized in patients with cancer as it does not significantly increase inpatient mortality, complications, and LOS. These findings support the need for an increased utilization of FFR-guided PCI and further studies to evaluate its long-term impact.

Artificial intelligence-augmented analysis of contemporary procedural, mortality, and cost trends in carcinoid heart disease in a large national cohort with a focus on the "forgotten pulmonic valve".

Background: Carcinoid heart disease is increasingly recognized and challenging to manage due to limited outcomes data. This is the largest known cohort study of valvular pathology, treatment (including pulmonary and tricuspid valve replacements [PVR and TVR]), dispairties, mortality, and cost in patients with malignant carcinoid tumor (MCT). Methods: Machine learning-augmented propensity score-adjusted multivariable regression was conducted for clincal outcomes in the 2016-2018 U.S. National Inpatient Sample (NIS). Regression models were weighted by the complex survey design and adjusted for known confounders and the likelihood of undergoing valvular procedures. Results: Among 101,521,656 hospitalizations, 55,910 (0.06%) had MCT. Patients with MCT vs. those without had significantly higher inpatient mortality (2.93 vs. 2.04%, p = 0.002), longer mean length of stay (12.20 vs. 4.62, p < 0.001), and increased mean total cost of stay ($70,252.18 vs. 51,092.01, p < 0.001). There was a step-wise increased rate of TVR and PVR with each subsequent year, with significantly more TV (0.16% vs. 0.01, p < 0.001) and PV (0.03 vs. 0.00, p = 0.040) diagnosed with vs. without MCT for 2016, with comparable trends in 2017 and 2018. There were no significant procedural disparities among patients with MCT for sex, race, income, urban density, or geographic region, except in 2017, when the highest prevalence of PV procedures were performed in the Western North at 50.00% (p = 0.034). In machine learning and propensity score augmented multivariable regression, MCT did not significantly increase the likelihood of TVR or PVR. In sub-group analysis restricted to MCT, neither TVR nor PVR significantly increased mortality, though it did increase cost (respectively, $141,082.30, p = 0.015; $355,356.40, p = 0.012). Conclusion: This analysis reflects a favorable trend in recognizing the need for TVR and PVR in patients with MCT, with associated increased cost but not mortality. Our study also suggests that pulmonic valve pathology is increasingly recognized in MCT as reflected by the upward trend in PVRs. Further research and updated societal guidelines may need to focus on the "forgotten pulmonic valve" to improve outcomes and disparities in this understudied patient population.

Racial and Socioeconomic Disparities in Out-Of-Hospital Cardiac Arrest Outcomes: Artificial Intelligence-Augmented Propensity Score and Geospatial Cohort Analysis of 3,952 Patients.

INTRODUCTION: Social disparities in out-of-hospital cardiac arrest (OHCA) outcomes are preventable, costly, and unjust. We sought to perform the first large artificial intelligence- (AI-) guided statistical and geographic information system (GIS) analysis of a multiyear and multisite cohort for OHCA outcomes (incidence and poor neurological disposition). METHOD: We conducted a retrospective cohort analysis of a prospectively collected multicenter dataset of adult patients who sequentially presented to Houston metro area hospitals from 01/01/07-01/01/16. Then AI-based machine learning (backward propagation neural network) augmented multivariable regression and GIS heat mapping were performed. RESULTS: Of 3,952 OHCA patients across 38 hospitals, African Americans were the most likely to suffer OHCA despite representing a significantly lower percentage of the population (42.6 versus 22.8%; p < 0.001). Compared to Caucasians, they were significantly more likely to have poor neurological disposition (OR 2.21, 95%CI 1.25-3.92; p=0.006) and be discharged to a facility instead of home (OR 1.39, 95%CI 1.05-1.85; p=0.023). Compared to the safety net hospital system primarily serving poorer African Americans, the university hospital serving primarily higher income commercially and Medicare insured patients had the lowest odds of death (OR 0.45, p < 0.001). Each additional $10,000 above median household income was associated with a decrease in the total number of cardiac arrests per zip code by 2.86 (95%CI -4.26- -1.46; p < 0.001); zip codes with a median income above $54,600 versus the federal poverty level had 14.62 fewer arrests (p < 0.001). GIS maps showed convergence of the greater density of poor neurologic outcome cases and greater density of poorer African American residences. CONCLUSION: This large, longitudinal AI-guided analysis statistically and geographically identifies racial and socioeconomic disparities in OHCA outcomes in a way that may allow targeted medical and public health coordinated efforts to improve clinical, cost, and social equity outcomes.

Stress cardiomyopathy in hospitalized patients with cancer: machine learning analysis by primary malignancy type.

AIMS: Previous studies have shown that patients with stress (Takotsubo) cardiomyopathy (SC) and cancer have higher in-hospital mortality than patients with SC alone. No studies have examined outcomes in patients with active cancer and SC compared to patients with active cancer without SC. We aimed to assess the potential association between primary malignancy type and SC and their shared interaction with inpatient mortality. METHODS AND RESULTS: We analysed SC by primary malignancy type with propensity score adjusted multivariable regression and machine learning analysis using the 2016 United States National Inpatient Sample. Of 30 195 722 adult hospitalized patients, 4 719 591 had active cancer, of whom 568 239 had SC. The mean age of patients with cancer and SC was 69.1, of which 74.7% were women. Among patients with cancer, those with SC were more likely to be female and have white race, Medicare insurance, hypertension, heart failure with reduced ejection fraction, obesity, cerebrovascular disease, anaemia, and chronic obstructive pulmonary disease (P < 0.003 for all). In machine learning-augmented, propensity score multivariable regression adjusted for age, race, and income, only lung cancer [OR 1.25; 95% CI: 1.08-1.46; P = 0.003] and breast cancer [OR 1.81; 95% CI: 1.62-2.02; P < 0.001] were associated with a significantly increased likelihood of SC. Neither SC alone nor having both SC and cancer was significantly associated with in-hospital mortality. The presence of concomitant SC and breast cancer was significantly associated with reduced mortality (OR 0.48; 95% CI: 0.25-0.94; P = 0.032). CONCLUSIONS: This analysis demonstrates that primary malignancy type influences the likelihood of developing SC. Further studies will be necessary to delineate characteristics in patients with lung cancer and breast cancer which contribute to development of SC. Additional investigation should confirm lower mortality in patients with SC and breast cancer and determine possible explanations and protective factors.

TAVR and cancer: machine learning-augmented propensity score mortality and cost analysis in over 30 million patients.

INTRODUCTION: Cardiovascular disease (CVD) and cancer are the top mortality causes globally, yet little is known about how the diagnosis of cancer affects treatment options in patients with hemodynamically compromising aortic stenosis (AS). Patients with cancer often are excluded from aortic valve replacement (AVR) trials including trials with transcatheter AVR (TAVR) and surgical AVR (SAVR). This study looks at how cancer may influence treatment options and assesses the outcome of patients with cancer who undergo SAVR or TAVR intervention. Additionally, we sought to quantitate and compare both clinical and cost outcomes for patients with and without cancer. METHODS: This population-based case-control study uses the most recent year available National Inpatient Sample (NIS (2016) from the United States Department of Health and Human Services' Agency for Healthcare Research and Quality (AHRQ). Machine learning augmented propensity score adjusted multivariable regression was conducted based on the likelihood of undergoing TAVR versus medical management (MM) and TAVR versus SAVR with model optimization supported by backward propagation neural network machine learning. RESULTS: Of the 30,195,722 total hospital admissions, 39,254 (0.13%) TAVRs were performed, with significantly fewer performed in patients with versus without cancer even in those of comparable age and mortality risk (23.82% versus 76.18%, p < 0.001) despite having similar hospital and procedural mortality. Multivariable regression in patients with cancer demonstrated that mortality was similar for TAVR, MM, and SAVR, though LOS and cost was significantly lower for TAVR versus MM and comparable for TAVR versus SAVR. Patients with prostate cancer constituted the largest primary cancer among TAVR patients including those with metastatic disease. There were no significant race or geographic disparities for TAVR mortality. DISCUSSION: Comparison of aortic valve intervention in patients with and without cancer suggests that interventions are underutilized in the cancer population. This study suggests that patients with cancer including those with metastasis have similar inpatient outcomes to patients without cancer. Further, patients who have symptomatic AS and those with higher risk aortic valve disease should be offered the benefit of intervention. Modern techniques have reduced intervention-related adverse events, provided improved quality of life, and appear to be cost effective; these advantages should not necessarily be denied to patients with co-existing cancer.

Percutaneous Coronary Intervention in Patients With Gynecological Cancer: Machine Learning-Augmented Propensity Score Mortality and Cost Analysis for 383,760 Patients.

BACKGROUND: Despite the growing number of patients with both coronary artery disease and gynecological cancer, there are no nationally representative studies of mortality and cost effectiveness for percutaneous coronary interventions (PCI) and this cancer type. METHODS: Backward propagation neural network machine learning supported and propensity score adjusted multivariable regression was conducted for the above outcomes in this case-control study of the 2016 National Inpatient Sample (NIS), the United States' largest all-payer hospitalized dataset. Regression models were fully adjusted for age, race, income, geographic region, cancer metastases, mortality risk, and the likelihood of undergoing PCI (and also with length of stay [LOS] for cost). Analyses were also adjusted for the complex survey design to produce nationally representative estimates. Centers for Disease Control and Prevention (CDC)-based cost effectiveness ratio (CER) analysis was performed. RESULTS: Of the 30,195,722 hospitalized patients meeting criteria, 1.27% had gynecological cancer of whom 0.02% underwent PCI including 0.04% with metastases. In propensity score adjusted regression among all patients, the interaction of PCI and gynecological cancer (vs. not having PCI) significantly reduced mortality (OR 0.53, 95%CI 0.36-0.77; p = 0.001) while increasing LOS (Beta 1.16 days, 95%CI 0.57-1.75; p < 0.001) and total cost (Beta $31,035.46, 95%CI 26758.86-35312.06; p < 0.001). Among gynecological cancer patients, mortality was significantly reduced by PCI (OR 0.58, 95%CI 0.39-0.85; p = 0.006) and being in East North Central, West North Central, South Atlantic, and Mountain regions (all p < 0.03) compared to New England. PCI reduced mortality but not significantly for metastatic patients (OR 0.74, 95%CI 0.32-1.71; p = 0.481). Eighteen extra gynecological cancer patients' lives were saved with PCI for a net national cost of $3.18 billion and a CER of $176.50 million per averted death. CONCLUSION: This large propensity score analysis suggests that PCI may cost inefficiently reduce mortality for gynecological cancer patients, amid income and geographic disparities in outcomes.

Effect of culinary education curriculum on Mediterranean diet adherence and food cost savings in families: a randomised controlled trial.

OBJECTIVE: Diet-related diseases are the leading cause of morbidity and mortality in the USA. While the critical aspects of a healthy diet are well known, the relationship between community-based, teaching kitchen education and dietary behaviours is unclear. We examined the effect of a novel culinary medicine education programme on Mediterranean diet adherence and food cost savings. DESIGN: Families were randomised to a hands-on, teaching kitchen culinary education class (n = 18) or non-kitchen-based dietary counselling (n = 23) for 6 weeks. The primary outcome was adherence to the validated nine-point Mediterranean diet score, and the secondary outcome was food cost savings per family. SETTING: The Goldring Center for Culinary Medicine, a community teaching kitchen in New Orleans. PARTICIPANTS: Families (n = 41) of at least one child and one parent. RESULTS: Compared with families receiving traditional dietary counselling, those participating in hands-on, kitchen-based nutrition education were nearly three times as likely to follow a Mediterranean dietary pattern (OR 2·93, 95% CI 1·73, 4·95; P  <  0·001), experiencing a 0·43-point increase in Mediterranean diet adherence after 6 weeks (B  =  0·43; P  <  0·001). Kitchen-based nutrition education projects to save families $US 21·70 per week compared with standard dietary counselling by increasing the likelihood of consuming home-prepared v. commercially-prepared meals (OR 1·56, 95% CI 1·08, 2·25; P  =  0·018). CONCLUSIONS: Community-based culinary medicine education improves Mediterranean diet adherence and associates with food cost savings among a diverse sample of families. Hands-on culinary medicine education may be a novel evidence-based tool to teach healthful dietary habits and prevent chronic disease.

Etiological Role of Diet in 30-Day Readmissions for Heart Failure: Implications for Reducing Heart Failure-Associated Costs via Culinary Medicine.

Background. Reducing the under-30-day readmission for heart failure (HF) patients is a modifiable quality-of-care measure, yet the role of diet in HF readmissions and cost-effective HF care remain ill-defined. Methods. Medical chart review was conducted to determine cause(s) for HF treatment failure. Randomized controlled trial-backed machine learning models were employed to assess the relationship of culinary medicine education with HF 30-day readmission rate and cost. Results. Of 1031 HF admissions, 130 occurred within 30 days of discharge (12.61%.) Nearly two-thirds of individuals were male (64.02%), while the mean age and median length of stay were 64.33 ± 14.02 and 2, respectively. Medication noncompliance (34.62%) was the most common etiology for 30-day readmissions, followed by dietary noncompliance (16.92%), comorbidity (16.92%), a combination of dietary and medication noncompliance (10%), HF exacerbation (10%), iatrogenic (10%), and drug abuse (1.54%). Medication noncompliance contributed to the highest gross charge by readmission, costing a total of $1 802 096. Compared with traditional care, culinary medicine education for HF patients would prevent 93 HF readmissions and save $3.9 million in an estimated 4-year period. Conclusion. Though pharmacological treatment remains a focal point of HF management, diet-based approaches may improve tertiary HF prevention and reduce HF-associated health care expenditures.

Racial and regional disparity in liver transplant allocation.

BACKGROUND: Sources of liver transplant disparities are not understood adequately, particularly in terms of race and region. METHODS: Fixed effects multivariate logistic regression augmented by modified forward and backward stepwise regression of transplanted patients from the United Network for Organ Sharing Standard Transplant Analysis and Research database (1985-2016) was performed to assess causal inference of such disparities. RESULTS: In the study sample (N = 258,602), significant disparities in the odds of receiving a liver were found: African Americans odds ratio 1.12 (95% confidence interval, 1.08-1.17), Asians 1.12 (95% confidence interval, 1.07-1.18), females 0.80 (95% confidence interval, 0.78-0.83), and malignancy 1.18 (95% confidence interval, 1.13-1.22). Region 7 (IL, MN, ND, SD, and WI) was set as the reference level since its transplantation rate most closely approximated the sex and race-matched rate of the national post-Share 35 average. Significant racial disparities by region were identified using Caucasian Region 7 as the reference: Hispanic Region 9 (New York, West Vermont) 1.22 (1.02-1.45), Hispanic Region 1 (New England) 1.26 (1.01-1.57), Hispanic Region 4 (Oklahoma, TX) 1.23 (1.05-1.43), and Asian Region 4 (Oklahoma, TX) 1.35 (1.05-1.73). CONCLUSION: Despite numerous adjustments to liver allocation, we identified with causal inference statistics on a large dataset spanning ≥30 years there remain racial and regional overweighting.